{"id":"https://openalex.org/W1964983104","doi":"https://doi.org/10.1145/1857947.1857949","title":"Behavioral Targeting","display_name":"Behavioral Targeting","publication_year":2010,"publication_date":"2010-10-01","ids":{"openalex":"https://openalex.org/W1964983104","doi":"https://doi.org/10.1145/1857947.1857949","mag":"1964983104"},"language":"en","primary_location":{"id":"doi:10.1145/1857947.1857949","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1857947.1857949","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100359095","display_name":"Ye Chen","orcid":"https://orcid.org/0000-0002-1080-7671"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4210105678","display_name":"Microsoft (Finland)","ror":"https://ror.org/01nehjf29","country_code":"FI","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210105678"]}],"countries":["FI","US"],"is_corresponding":true,"raw_author_name":"Ye Chen","raw_affiliation_strings":["Microsoft Corporation","MICROSOFT CORP"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation","institution_ids":["https://openalex.org/I4210105678"]},{"raw_affiliation_string":"MICROSOFT CORP","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110057155","display_name":"Dmitry Pavlov","orcid":null},"institutions":[{"id":"https://openalex.org/I58957048","display_name":"Yandex (Russia)","ror":"https://ror.org/04dbch786","country_code":"RU","type":"company","lineage":["https://openalex.org/I58957048"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Dmitry Pavlov","raw_affiliation_strings":["Yandex Labs"],"affiliations":[{"raw_affiliation_string":"Yandex Labs","institution_ids":["https://openalex.org/I58957048"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089723214","display_name":"John Canny","orcid":"https://orcid.org/0000-0002-7161-7927"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John F. Canny","raw_affiliation_strings":["University of California, Berkeley"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100359095"],"corresponding_institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I4210105678"],"apc_list":null,"apc_paid":null,"fwci":1.4245,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81223027,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"4","issue":"4","first_page":"1","last_page":"31"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8551079034805298},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6500366926193237},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.4383270740509033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4331856369972229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37590423226356506},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32850146293640137},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1745709776878357}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8551079034805298},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6500366926193237},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.4383270740509033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4331856369972229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37590423226356506},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32850146293640137},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1745709776878357},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1857947.1857949","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1857947.1857949","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1749904362","https://openalex.org/W1839144526","https://openalex.org/W1866172591","https://openalex.org/W1971547598","https://openalex.org/W1973628995","https://openalex.org/W1982854196","https://openalex.org/W1986966428","https://openalex.org/W1994371611","https://openalex.org/W2012852390","https://openalex.org/W2034053794","https://openalex.org/W2060204748","https://openalex.org/W2073448073","https://openalex.org/W2097533650","https://openalex.org/W2135029798","https://openalex.org/W2141218329","https://openalex.org/W2173213060","https://openalex.org/W4235138236","https://openalex.org/W4302853952"],"related_works":["https://openalex.org/W1594844924","https://openalex.org/W2909382770","https://openalex.org/W2461970972","https://openalex.org/W2364921833","https://openalex.org/W2388030554","https://openalex.org/W2302028273","https://openalex.org/W1525643724","https://openalex.org/W2961085424","https://openalex.org/W2067938758","https://openalex.org/W2380023786"],"abstract_inverted_index":{"Behavioral":[0],"targeting":[1],"(BT)":[2],"leverages":[3],"historical":[4],"user":[5,29,38,75],"behavior":[6],"to":[7,13,15,50,186,203,216],"select":[8],"the":[9,61,72,80,100,106,143,167,181],"ads":[10],"most":[11],"relevant":[12],"users":[14],"display.":[16],"The":[17],"state-of-the-art":[18],"of":[19,142,147,166,169,183,199,222],"BT":[20,51],"derives":[21],"a":[22,44,112],"linear":[23],"Poisson":[24],"regression":[25],"model":[26,109,206],"from":[27,37,71,111],"fine-grained":[28],"behavioral":[30],"data":[31,133,194,202],"and":[32,42,47,60,128,137,196,201,237],"predicts":[33],"click-through":[34],"rate":[35],"(CTR)":[36],"history.":[39],"We":[40,208],"designed":[41],"implemented":[43],"highly":[45],"scalable":[46],"efficient":[48,193],"solution":[49],"using":[52,233],"Hadoop":[53],"MapReduce":[54,124],"framework.":[55],"With":[56],"our":[57,92,118,211],"parallel":[58],"algorithm":[59,127,156],"resulting":[62],"system,":[63],"we":[64,228],"can":[65,84],"build":[66],"above":[67],"450":[68],"BT-category":[69],"models":[70,200],"entire":[73],"Yahoo\u2019s":[74],"base":[76],"within":[77],"one":[78,83],"day,":[79],"scale":[81],"that":[82,130,178,210],"not":[85],"even":[86],"imagine":[87],"with":[88,157],"prior":[89],"systems.":[90],"Moreover,":[91],"approach":[93],"has":[94],"yielded":[95],"20%":[96],"CTR":[97],"lift":[98],"over":[99],"existing":[101],"production":[102],"system":[103],"by":[104],"leveraging":[105],"well-grounded":[107],"probabilistic":[108],"fitted":[110],"much":[113],"larger":[114],"training":[115],"dataset.":[116],"Specifically,":[117],"major":[119],"contributions":[120,215],"include:":[121],"(1)":[122],"A":[123],"statistical":[125],"learning":[126,189,220],"implementation":[129],"achieve":[131],"optimal":[132],"parallelism,":[134,136],"task":[135],"load":[138],"balance":[139],"in":[140,225],"spite":[141],"typically":[144],"skewed":[145],"distribution":[146],"domain":[148],"data.":[149],"(2)":[150],"An":[151,174],"in-place":[152],"feature":[153],"vector":[154],"generation":[155],"strict":[158],"linear-time":[159],"complexity":[160],"O":[161],"(":[162],"n":[163],")":[164],"regardless":[165],"granularity":[168],"sliding":[170],"target":[171],"window.":[172],"(3)":[173],"in-memory":[175],"caching":[176],"scheme":[177],"significantly":[179],"reduces":[180],"number":[182],"disk":[184],"IOs":[185],"make":[187],"large-scale":[188,218],"practical.":[190],"(4)":[191],"Highly":[192],"structures":[195],"sparse":[197],"representations":[198],"enable":[204],"fast":[205],"updates.":[207],"believe":[209],"work":[212],"makes":[213],"significant":[214],"solving":[217],"machine":[219],"problems":[221],"industrial":[223,234],"relevance":[224],"general.":[226],"Finally,":[227],"report":[229],"comprehensive":[230],"experimental":[231],"results,":[232],"proprietary":[235],"codebase":[236],"datasets.":[238]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}